The interaction of aseismic and seismic slip before and after an earthquake is fundamental for both earthquake nucleation and postseismic stress relaxation. However, it can be difficult to determine where and when aseismic slip occurs within the seismogenic zone because geodetic techniques are limited to detecting moderate to large slip amplitudes or long duration small slip amplitudes. Here, we use repeating earthquakes (earthquakes that re-rupture the same fault patch) as a proxy for aseismic slip during the 2011 Prague, Oklahoma earthquake sequence. We find that aseismic slip in the Prague earthquake sequence occurs both within the granitic basement and the overlying sedimentary rocks. The repeating earthquakes show that patches of aseismic slip are mostly located at fault intersections. These fault intersections hosted possible mainshock slip, abundant aftershocks, and afterslip. We estimate that ∼40% of the aftershocks are driven by afterslip. We interpret that aseismic slip occurs at fault intersections where stress heterogeneity creates patches of lower stress that are stable within a nonsteady state, rate-state framework.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JB024431","usgsCitation":"Okamoto, K., Savage, H., Cochran, E.S., and Keranen, K.M., 2022, Stress heterogeneity as a driver of aseismic slip during the 2011 Prague, Oklahoma aftershock sequence: Journal of Geophysical Research Solid Earth, v. 127, no. 8, e2022JB024431, 15 p., https://doi.org/10.1029/2022JB024431.","productDescription":"e2022JB024431, 15 p.","ipdsId":"IP-138835","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":446761,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jb024431","text":"Publisher Index Page"},{"id":406831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","city":"Prague","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.7,\n 35.44\n ],\n [\n -96.9,\n 35.44\n ],\n [\n -96.9,\n 35.59\n ],\n [\n -96.7,\n 35.59\n ],\n [\n -96.7,\n 35.44\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Okamoto, Kristina","contributorId":296586,"corporation":false,"usgs":false,"family":"Okamoto","given":"Kristina","email":"","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":851914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Savage, Heather M.","contributorId":296588,"corporation":false,"usgs":false,"family":"Savage","given":"Heather","middleInitial":"M.","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":851915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":851916,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keranen, Katie M.","contributorId":197630,"corporation":false,"usgs":false,"family":"Keranen","given":"Katie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":851917,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70235698,"text":"sim3489 - 2022 - Geologic map of MTM −10022 and −15022 quadrangles, Morava Valles and Margaritifer basin, Mars","interactions":[],"lastModifiedDate":"2023-03-20T18:16:53.878209","indexId":"sim3489","displayToPublicDate":"2022-08-15T12:33:16","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3489","displayTitle":"Geologic Map of MTM −10022 and −15022 Quadrangles, Morava Valles and Margaritifer Basin, Mars","title":"Geologic map of MTM −10022 and −15022 quadrangles, Morava Valles and Margaritifer basin, Mars","docAbstract":"The landscape in Mars Transverse Mercator (MTM) −10022 and −15022 quadrangles (lat −7.5° N. to −17.5° N. between long 335° E. and 340° E.) in Margaritifer Terra preserves a record of sedimentary and alluvial deposits, volcanic and tectonic structures, and erosional landforms that record a long and complex geologic and geomorphic history. MTM −10022 and −15022 quadrangles primarily encompass Morava Valles, the terminus of the Samara-Himera and Paraná-Loire valley networks, the broad catchment informally named Margaritifer basin, and Margaritifer Chaos. Morava Valles is the lowermost reach of the northward draining mesoscale outflow system that consists of Uzboi Vallis, Ladon Valles, and Morava Valles, was sourced from flow out of Argyre basin, and incises across and between the ancient Ladon and Holden impact basins. The broad-scale topography and surface relief within the map, including the topographic low occupied by Margaritifer basin, were largely shaped during the Noachian by the formation of the Holden, Ladon, and Ares impact basins and the Chryse trough. Multiple processes modified the ancient surface until the Late Noachian and resulted in the formation of the terra unit that forms the widely exposed surface. Later resurfacing associated with likely sedimentary and volcanic processes modified predominantly lower elevation surfaces and basins during the Late Noachian into at least the Hesperian. Sedimentary processes during the Late Noachian were dominated by fluvial incision of the Samara-Himera and Paraná-Loire valley networks and discharge related to the dissection of Morava Valles that drained Ladon basin. The history of geomorphic activity within Margaritifer basin was more complex and was likely dominated by the evolution of Morava Valles relative to the formation of the valley networks. The floor of Margaritifer basin preserves likely lacustrine plains related to sedimentation in water ponded during early discharge from Morava Valles, which were later embayed by volcanic plains. Crater densities and cross-cutting relations indicate Margaritifer basin evolved over a relatively short period of geologic time. The timing of the last drainage out of Morava Valles is not well constrained but could have occurred during the Hesperian. Structural collapse and the formation of the Margaritifer Chaos and other chaotic terrain formed by the release of subsurface water that may have been related to volcanic activity along the southern margin of Margaritifer basin. Final geomorphic events within the map region include the formation of Late Hesperian to perhaps Amazonian alluvial fans within some craters and isolated mass wasting on steep slopes. A final, variable veneer associated with locally occurring impacts and redistribution of fine-grained material by eolian processes resulted in the landscape observed today.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3489","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Wilson, S.A., Grant, J.A., and Williams, K.K., 2022, Geologic map of MTM −10022 and −15022 quadrangles, Morava Valles and Margaritifer basin, Mars: U.S. Geological Survey Scientific Investigations Map 3489, pamphlet 11 p., 1 sheet, scale 1:500,000, https://doi.org/10.3133/sim3489.","productDescription":"Report: iv, 11 p.; 1 Sheet: 45.73 x 54.01 inches; 2 Databases; Metadata; Read Me","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-118399","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":435729,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JJZDWR","text":"USGS data release","linkHelpText":"Interactive Map: USGS SIM 3489 Geologic Map of MTM −10022 and −15022 Quadrangles, Morava Valles and Margaritifer Basin, Mars"},{"id":405424,"rank":8,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P9JJZDWR","text":"Interactive map","linkHelpText":"- Geologic Map of MTM −10022 and −15022 Quadrangles, Morava Valles and Margaritifer Basin, Mars"},{"id":405141,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_supdata.zip","text":"Supplemental Data","size":"500 MB","linkFileType":{"id":6,"text":"zip"}},{"id":405140,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_sheet.pdf","text":"Map sheet","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Geologic Map of MTM −10022 and −15022 Quadrangles, Morava Valles and Margaritifer Basin, Mars"},{"id":405138,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_metadata.xml","size":"7 KB","linkFileType":{"id":8,"text":"xml"}},{"id":405137,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_pamphlet.pdf","text":"Pamphlet","size":"700 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":405136,"rank":2,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_gis.zip","text":"GIS Files","size":"55 MB","linkFileType":{"id":6,"text":"zip"}},{"id":405135,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3489/covrthb.jpg"},{"id":405139,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_readme.txt","size":"5 KB","linkFileType":{"id":2,"text":"txt"}}],"otherGeospatial":"Margaritifer basin, Mars, Morava Valles basin","contact":"Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
Mathematical modelling is used during disease outbreaks to compare control interventions. Using multiple models, the best method to combine model recommendations is unclear. Existing methods weight model projections, then rank control interventions using the combined projections, presuming model outputs are directly comparable. However, the way each model represents the epidemiological system will vary. We apply electoral vote-processing rules to combine model-generated rankings of interventions. Combining rankings of interventions, instead of combining model projections, avoids assuming that projections are comparable as all comparisons of projections are made within each model. We investigate four rules: First-past-the-post, Alternative Vote (AV), Coombs Method and Borda Count. We investigate rule sensitivity by including models that favour only one action or including those that rank interventions randomly. We investigate two case studies: the 2014 Ebola outbreak in West Africa (37 compartmental models) and a hypothetical foot-and-mouth disease outbreak in UK (four individual-based models). The Coombs Method was least susceptible to adding models that favoured a single action, Borda Count and AV were most susceptible to adding models that ranked interventions randomly. Each rule chose the same intervention as when ranking interventions by mean projections, suggesting that combining rankings provides similar recommendations with fewer assumptions about model comparability.
","language":"English","publisher":"Royal Society Publishing","doi":"10.1098/rsta.2021.0314","usgsCitation":"Probert, W.J., Nicol, S., Ferrari, M.J., Li, S., Shea, K., Tildesley, M.J., and Runge, M.C., 2022, Vote-processing rules for combining control recommendations from multiple models: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, v. 380, no. 2233, 20210314, 20 p., https://doi.org/10.1098/rsta.2021.0314.","productDescription":"20210314, 20 p.","ipdsId":"IP-142649","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446776,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rsta.2021.0314","text":"Publisher Index Page"},{"id":405458,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"380","issue":"2233","noUsgsAuthors":false,"publicationDate":"2022-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Probert, William J.M.","contributorId":295477,"corporation":false,"usgs":false,"family":"Probert","given":"William","email":"","middleInitial":"J.M.","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":849532,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nicol, Sam","contributorId":171610,"corporation":false,"usgs":false,"family":"Nicol","given":"Sam","email":"","affiliations":[{"id":26927,"text":"CSIRO, Australia","active":true,"usgs":false}],"preferred":false,"id":849533,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferrari, Matthew J. 0000-0001-5251-8168","orcid":"https://orcid.org/0000-0001-5251-8168","contributorId":216186,"corporation":false,"usgs":false,"family":"Ferrari","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":849534,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Shou-Li","contributorId":193644,"corporation":false,"usgs":false,"family":"Li","given":"Shou-Li","email":"","affiliations":[],"preferred":false,"id":849535,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shea, Katriona 0000-0002-7607-8248","orcid":"https://orcid.org/0000-0002-7607-8248","contributorId":193646,"corporation":false,"usgs":false,"family":"Shea","given":"Katriona","email":"","affiliations":[],"preferred":false,"id":849536,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tildesley, Michael J.","contributorId":126971,"corporation":false,"usgs":false,"family":"Tildesley","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6620,"text":"University of Nottingham, School of Biology","active":true,"usgs":false}],"preferred":false,"id":849537,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":849538,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70235876,"text":"70235876 - 2022 - Vote-processing rules for combining control recommendations from multiple models","interactions":[],"lastModifiedDate":"2022-08-24T11:38:20.39071","indexId":"70235876","displayToPublicDate":"2022-08-15T06:36:11","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3047,"text":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Vote-processing rules for combining control recommendations from multiple models","docAbstract":"Mathematical modelling is used during disease outbreaks to compare control interventions. Using multiple models, the best method to combine model recommendations is unclear. Existing methods weight model projections, then rank control interventions using the combined projections, presuming model outputs are directly comparable. However, the way each model represents the epidemiological system will vary. We apply electoral vote-processing rules to combine model-generated rankings of interventions. Combining rankings of interventions, instead of combining model projections, avoids assuming that projections are comparable as all comparisons of projections are made within each model. We investigate four rules: First-past-the-post, Alternative Vote (AV), Coombs Method and Borda Count. We investigate rule sensitivity by including models that favour only one action or including those that rank interventions randomly. We investigate two case studies: the 2014 Ebola outbreak in West Africa (37 compartmental models) and a hypothetical foot-and-mouth disease outbreak in UK (four individual-based models). The Coombs Method was least susceptible to adding models that favoured a single action, Borda Count and AV were most susceptible to adding models that ranked interventions randomly. Each rule chose the same intervention as when ranking interventions by mean projections, suggesting that combining rankings provides similar recommendations with fewer assumptions about model comparability.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rsta.2021.0314","usgsCitation":"Probert, W.J., Nicol, S., Ferrari, M.J., Li, S., Shea, K., Tildesley, M.J., and Runge, M.C., 2022, Vote-processing rules for combining control recommendations from multiple models: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, v. 380, no. 2233, 20210314, 20 p., https://doi.org/10.1098/rsta.2021.0314.","productDescription":"20210314, 20 p.","ipdsId":"IP-136798","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":467169,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rsta.2021.0314","text":"Publisher Index Page"},{"id":405525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"380","issue":"2233","noUsgsAuthors":false,"publicationDate":"2022-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Probert, William JM","contributorId":295493,"corporation":false,"usgs":false,"family":"Probert","given":"William","email":"","middleInitial":"JM","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":849595,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nicol, Sam","contributorId":171610,"corporation":false,"usgs":false,"family":"Nicol","given":"Sam","email":"","affiliations":[{"id":26927,"text":"CSIRO, Australia","active":true,"usgs":false}],"preferred":false,"id":849596,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferrari, Matthew J. 0000-0001-5251-8168","orcid":"https://orcid.org/0000-0001-5251-8168","contributorId":216186,"corporation":false,"usgs":false,"family":"Ferrari","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":849597,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Shou-Li","contributorId":193644,"corporation":false,"usgs":false,"family":"Li","given":"Shou-Li","email":"","affiliations":[],"preferred":false,"id":849598,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shea, Katriona 0000-0002-7607-8248","orcid":"https://orcid.org/0000-0002-7607-8248","contributorId":193646,"corporation":false,"usgs":false,"family":"Shea","given":"Katriona","email":"","affiliations":[],"preferred":false,"id":849599,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tildesley, Michael J.","contributorId":126971,"corporation":false,"usgs":false,"family":"Tildesley","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6620,"text":"University of Nottingham, School of Biology","active":true,"usgs":false}],"preferred":false,"id":849600,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":849601,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256627,"text":"70256627 - 2022 - Deep and machine learning image classification of coastal wetlands using unpiloted aircraft system multispectral images and lidar datasets","interactions":[],"lastModifiedDate":"2024-08-27T16:04:33.044121","indexId":"70256627","displayToPublicDate":"2022-08-13T10:55:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Deep and machine learning image classification of coastal wetlands using unpiloted aircraft system multispectral images and lidar datasets","docAbstract":"The recent developments of new deep learning architectures create opportunities to accurately classify high-resolution unoccupied aerial system (UAS) images of natural coastal systems and mandate continuous evaluation of algorithm performance. We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). Twelve combinations of spectral bands and CHMs were used. Our results using the spectral bands showed that the U-Net (83.80–85.27% overall accuracy) and the DeepLabV3 (75.20–83.50% overall accuracy) deep learning techniques outperformed the SVM (60.50–71.10% overall accuracy) and the RF (57.40–71.0%) machine learning algorithms. The addition of the CHM to the spectral bands slightly increased the overall accuracy as a whole in the deep learning models, while the addition of a CHM notably improved the SVM and RF results. Similarly, using bands outside the three spectral bands, namely, near-infrared and red edge, increased the performance of the machine learning classifiers but had minimal impact on the deep learning classification results. The difference in the overall accuracies produced by using UAS-based lidar and SfM point clouds, as supplementary geometrical information, in the classification process was minimal across all classification techniques. Our results highlight the advantage of using deep learning networks to classify high-resolution UAS images in highly diverse coastal landscapes. We also found that low-cost, three-visible-band imagery produces results comparable to multispectral imagery that do not risk a significant reduction in classification accuracy when adopting deep learning models.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14163937","usgsCitation":"Gonzalez Perez, A., Abd-Elrahman, A., Wilkinson, B., Johnson, D.J., and Carthy, R., 2022, Deep and machine learning image classification of coastal wetlands using unpiloted aircraft system multispectral images and lidar datasets: Remote Sensing, v. 14, no. 16, 3937, 41 p., https://doi.org/10.3390/rs14163937.","productDescription":"3937, 41 p.","ipdsId":"IP-141836","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":446787,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14163937","text":"Publisher Index Page"},{"id":433203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Wolf Branch Creek Coastal Nature Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.43286683042422,\n 27.759840853994277\n ],\n [\n -82.46741924846995,\n 27.759840853994277\n ],\n [\n -82.46741924846995,\n 27.737037233479754\n ],\n [\n -82.43286683042422,\n 27.737037233479754\n ],\n [\n -82.43286683042422,\n 27.759840853994277\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"16","noUsgsAuthors":false,"publicationDate":"2022-08-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Gonzalez Perez, Ali","contributorId":341416,"corporation":false,"usgs":false,"family":"Gonzalez Perez","given":"Ali","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908380,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abd-Elrahman, Amr","contributorId":341417,"corporation":false,"usgs":false,"family":"Abd-Elrahman","given":"Amr","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908381,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilkinson, Benjamin","contributorId":239953,"corporation":false,"usgs":false,"family":"Wilkinson","given":"Benjamin","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908382,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Daniel J.","contributorId":197828,"corporation":false,"usgs":false,"family":"Johnson","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":908383,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carthy, Raymond 0000-0001-8978-5083","orcid":"https://orcid.org/0000-0001-8978-5083","contributorId":219303,"corporation":false,"usgs":true,"family":"Carthy","given":"Raymond","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908384,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247742,"text":"70247742 - 2022 - Brittle faulting at elevated temperature and vanishing effective stress","interactions":[],"lastModifiedDate":"2023-08-15T14:20:11.412815","indexId":"70247742","displayToPublicDate":"2022-08-13T09:12:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7501,"text":"JGR Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Brittle faulting at elevated temperature and vanishing effective stress","docAbstract":"If brittle fault strength depends only on friction, slip instability is discouraged at low effective normal stress, σ. Stress drop and the critical stiffness necessary for unstable sliding both vanish with σ; small earthquakes cannot occur. Very low σ is inferred in the source region of low-frequency earthquakes (LFEs) on the San Andreas fault (SAF). Moreover, if pore pressure, p, is undrained at low σ, then instabilities are prevented at all scales. This is due to dilatant strengthening which arises due to a dependence of porosity on strain rate. Dilatant strengthening is σ-independent and dominates at low σ. Undrained p is inferred over time scales of less than a few days for the SAF LFEs. Based on experiments that measure rapid contact overgrowth between 350 and 530°C at very low σ, fault failure controlled by time-dependent cementation is invoked as an explanation for the SAF LFEs. Because this “cohesion” is σ-independent, stress drops can occur at σ = 0. If in addition cohesion exceeds any dilatant strengthening during slip, cohesion dominates strength at low σ. Dilatancy measured in prior faulting and shear experiments indicate that at all stress levels steady-state porosity depends on σ in addition to strain rate. Moreover, porosity at low σ depends elastically on the confining and differential stresses. A model with these additional pore pressure effects, friction, and time-dependent cohesion, applied to the SAF LFEs produces stress drops, slip speeds, and durations that are consistent with the observations, when the shear-induced dilatancy is not extreme.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JB024335","usgsCitation":"Beeler, N.M., 2022, Brittle faulting at elevated temperature and vanishing effective stress: JGR Solid Earth, v. 127, no. 9, e2022JB024335, 30 p., https://doi.org/10.1029/2022JB024335.","productDescription":"e2022JB024335, 30 p.","ipdsId":"IP-132369","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":419812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"127","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-09-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Beeler, Nicholas M. 0000-0002-3397-8481 nbeeler@usgs.gov","orcid":"https://orcid.org/0000-0002-3397-8481","contributorId":2682,"corporation":false,"usgs":true,"family":"Beeler","given":"Nicholas","email":"nbeeler@usgs.gov","middleInitial":"M.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":880227,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70238501,"text":"70238501 - 2022 - Natural and anthropogenic landscape factors shape functional connectivity of an ecological specialist in urban Southern California","interactions":[],"lastModifiedDate":"2022-11-28T12:26:40.467915","indexId":"70238501","displayToPublicDate":"2022-08-13T06:21:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2774,"text":"Molecular Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Natural and anthropogenic landscape factors shape functional connectivity of an ecological specialist in urban Southern California","docAbstract":"Identifying how natural (i.e., unaltered by human activity) and anthropogenic landscape variables influence contemporary functional connectivity in terrestrial organisms can elucidate the genetic consequences of environmental change. We examine population genetic structure and functional connectivity among populations of a declining species, the Blainville's horned lizard (Phrynosoma blainvillii), in the urbanized landscape of the Greater Los Angeles Area in Southern California, USA. Using single nucleotide polymorphism data, we assessed genetic structure among populations occurring at the interface of two abutting evolutionary lineages, and at a fine scale among habitat fragments within the heavily urbanized area. Based on the ecology of P. blainvillii, we predicted which environmental variables influence population structure and gene flow and used gravity models to distinguish among hypotheses to best explain population connectivity. Our results show evidence of admixture between two evolutionary lineages and strong population genetic structure across small habitat fragments. We also show that topography, microclimate, and soil and vegetation types are important predictors of functional connectivity, and that anthropogenic disturbance, including recent fire history and urban development, are key factors impacting contemporary population dynamics. Examining how natural and anthropogenic sources of landscape variation affect contemporary population genetics is critical to understanding how to best manage sensitive species in a rapidly changing landscape.
Morphological processes often induce meter-scale elevation changes. When a volcano erupts, tracking such processes provides insights into the style and evolution of eruptive activity and related hazards. Compared to optical remote-sensing products, synthetic aperture radar (SAR) observes surface change during inclement weather and at night. Differential SAR interferometry estimates phase change between SAR acquisitions and is commonly applied to quantify deformation. However, large deformation or other coherence loss can limit its use. We develop a new approach applicable when repeated digital elevation models (DEMs) cannot be otherwise retrieved. Assuming an isotropic radar cross-section, we estimate meter-scale vertical morphological change directly from SAR amplitude images via an optimization method that utilizes a high-quality DEM. We verify our implementation through simulation of a collapse feature that we modulate onto topography. We simulate radar effects and recover the simulated collapse. To validate our method, we estimate elevation changes from TerraSAR-X stripmap images for the 2011–2012 eruption of Mount Cleveland. Our results reproduce those from two previous studies; one that used the same dataset, and another based on thermal satellite data. By applying this method to the 2019–2020 eruption of Shishaldin Volcano, Alaska, we generate elevation change time series from dozens of co-registered TerraSAR-X high-resolution spotlight images. Our results quantify previously unresolved cone growth in November 2019, collapses associated with explosions in December–January, and further changes in crater elevations into spring 2020. This method can be used to track meter-scale morphology changes for ongoing eruptions with low latency as SAR imagery becomes available.
Latest climate models project conditions for the end of this century that are generally outside of the human experience. These future conditions affect the resilience and sustainability of ecosystems, alter biogeographic zones, and impact biodiversity. Deep-time records of paleoclimate provide insight into the climate system over millions of years and provide examples of conditions very different from the present day, and in some cases similar to model projections for the future. In addition, the deep-time paleoecologic and sedimentologic archives provide insight into how species and habitats responded to past climate conditions. Thus, paleoclimatology provides essential context for the scientific understanding of climate change needed to inform resource management policy decisions. The Pliocene Epoch (5.3–2.6 Ma) is the most recent deep-time interval with relevance to future global warming. Analysis of marine sediments using a combination of paleoecology, biomarkers, and geochemistry indicates a global mean annual temperature for the Late Pliocene (3.6–2.6 Ma) ∼3°C warmer than the preindustrial. However, the inability of state-of-the-art climate models to capture some key regional features of Pliocene warming implies future projections using these same models may not span the full range of plausible future climate conditions. We use the Late Pliocene as one example of a deep-time interval relevant to management of biodiversity and ecosystems in a changing world. Pliocene reconstructed sea surface temperatures are used to drive a marine ecosystem model for the North Atlantic Ocean. Given that boundary conditions for the Late Pliocene are roughly analogous to present day, driving the marine ecosystem model with Late Pliocene paleoenvironmental conditions allows policymakers to consider a future ocean state and associated fisheries impacts independent of climate models, informed directly by paleoclimate information.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.972179","usgsCitation":"Dowsett, H.J., Jacobs, P., and de Mutsert, K., 2022, Using paleoecological data to inform decision making: A deep-time perspective: Frontiers in Ecology and Evolution, v. 10, 972179, 8 p., https://doi.org/10.3389/fevo.2022.972179.","productDescription":"972179, 8 p.","ipdsId":"IP-141870","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":446807,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.972179","text":"Publisher Index Page"},{"id":407619,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Dowsett, Harry J. 0000-0003-1983-7524","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":269579,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry","email":"","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":848146,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jacobs, Peter","contributorId":248861,"corporation":false,"usgs":false,"family":"Jacobs","given":"Peter","email":"","affiliations":[],"preferred":false,"id":848147,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"de Mutsert, Kim","contributorId":194503,"corporation":false,"usgs":false,"family":"de Mutsert","given":"Kim","email":"","affiliations":[],"preferred":false,"id":853377,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70252444,"text":"70252444 - 2022 - Growth and survival rates of dispersing free embryos and settled larvae of pallid sturgeon (Scaphirhynchus albus) in the Missouri River, Montana and North Dakota","interactions":[],"lastModifiedDate":"2024-03-25T13:52:21.885788","indexId":"70252444","displayToPublicDate":"2022-08-11T08:42:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Growth and survival rates of dispersing free embryos and settled larvae of pallid sturgeon (Scaphirhynchus albus) in the Missouri River, Montana and North Dakota","title":"Growth and survival rates of dispersing free embryos and settled larvae of pallid sturgeon (Scaphirhynchus albus) in the Missouri River, Montana and North Dakota","docAbstract":"We released nearly 1.0 million 1-day post-hatch (dph) and 5-dph pallid sturgeon (Scaphirhynchus albus) free embryos in the Missouri River on 1 July 2019 and sequentially captured survivors at multiple sites through a 240-km river reach to quantify daily growth and survival rates during the early life stages. Genetic analysis was used to assign captured fish to released family lots and known ages. Growth rate was similar (0.74–0.75 mm day−1) between the 1- and 5-dph age groups during the 3–4-day dispersal period when water temperature averaged 16.8 °C. Daily survival rate was 0.64 during 1–4 dph for the original 1-dph age group and 0.80 during 5–7 dph for the original 5-dph age group. Total survival during free embryo dispersal (hatch to 9 dph) was estimated as 0.0437. The transition from dispersing as free embryos to settling as benthic larvae was verified for fish originally released as 5 dph. Growth of settled larvae was quantified with a Gompertz model through 75 dph (9 September; 112 mm) when water temperature was 18.8–21.0 °C in the rearing areas. Settled larvae had an estimated daily survival rate of 0.96, and estimated total survival during 9–75 dph was 0.0714. This study provides the first empirical survival estimates for pallid sturgeon early life stages in natural settings and is one of few studies reporting similar information for other sturgeon species. Applications of this work extend to pallid sturgeon restoration programs where population models are being developed to predict recruitment potential and population responses to river management alternatives.
","language":"English","publisher":"Springer","doi":"10.1007/s10641-022-01294-w","usgsCitation":"Braaten, P., Holm, R., Powell, J.A., Heist, E., Buhman, A.C., Holley, C.T., Delonay, A.J., Haddix, T., Wilson, R., and Jacobson, R., 2022, Growth and survival rates of dispersing free embryos and settled larvae of pallid sturgeon (Scaphirhynchus albus) in the Missouri River, Montana and North Dakota: Environmental Biology of Fishes, v. 105, no. 8, p. 993-1014, https://doi.org/10.1007/s10641-022-01294-w.","productDescription":"12 p.; Data Release","startPage":"993","endPage":"1014","ipdsId":"IP-135304","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":446810,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10641-022-01294-w","text":"Publisher Index Page"},{"id":435731,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N2MFV8","text":"USGS data release","linkHelpText":"Pallid sturgeon free embryo drift and dispersal experiment data from the Upper Missouri River, Montana and North Dakota, 2019"},{"id":426965,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.89968504532749,\n 48.5\n ],\n [\n -106.89968504532749,\n 47.35255729260322\n ],\n [\n -103.54636278007621,\n 47.35255729260322\n ],\n [\n -103.54636278007621,\n 48.5\n ],\n [\n -106.89968504532749,\n 48.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"105","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Braaten, Patrick 0000-0003-3362-420X pbraaten@usgs.gov","orcid":"https://orcid.org/0000-0003-3362-420X","contributorId":152682,"corporation":false,"usgs":true,"family":"Braaten","given":"Patrick","email":"pbraaten@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holm, R.J.","contributorId":334977,"corporation":false,"usgs":false,"family":"Holm","given":"R.J.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":897180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powell, J. A.","contributorId":69916,"corporation":false,"usgs":false,"family":"Powell","given":"J.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":897181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heist, E.J.","contributorId":334978,"corporation":false,"usgs":false,"family":"Heist","given":"E.J.","affiliations":[{"id":13212,"text":"Southern Illinois University","active":true,"usgs":false}],"preferred":false,"id":897182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buhman, Amy C.","contributorId":334979,"corporation":false,"usgs":false,"family":"Buhman","given":"Amy","email":"","middleInitial":"C.","affiliations":[{"id":13212,"text":"Southern Illinois University","active":true,"usgs":false}],"preferred":false,"id":897183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holley, Colt Taylor 0000-0003-4172-4331","orcid":"https://orcid.org/0000-0003-4172-4331","contributorId":272272,"corporation":false,"usgs":true,"family":"Holley","given":"Colt","email":"","middleInitial":"Taylor","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897179,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"DeLonay, Aaron J. 0000-0002-3752-2799 adelonay@usgs.gov","orcid":"https://orcid.org/0000-0002-3752-2799","contributorId":2725,"corporation":false,"usgs":true,"family":"DeLonay","given":"Aaron","email":"adelonay@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897184,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haddix, T.M.","contributorId":334982,"corporation":false,"usgs":false,"family":"Haddix","given":"T.M.","affiliations":[{"id":39047,"text":"Montana Fish, Wildlife, and Parks","active":true,"usgs":false}],"preferred":false,"id":897186,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wilson, R.H.","contributorId":334984,"corporation":false,"usgs":false,"family":"Wilson","given":"R.H.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":897187,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jacobson, R. B. 0000-0002-8368-2064","orcid":"https://orcid.org/0000-0002-8368-2064","contributorId":92614,"corporation":false,"usgs":true,"family":"Jacobson","given":"R. B.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897185,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70236791,"text":"70236791 - 2022 - One shell of a problem: Cumulative threat analysis of male sea turtles indicates high anthropogenic threat for migratory individuals and Gulf of Mexico residents","interactions":[],"lastModifiedDate":"2023-06-08T14:55:58.163785","indexId":"70236791","displayToPublicDate":"2022-08-11T06:58:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"One shell of a problem: Cumulative threat analysis of male sea turtles indicates high anthropogenic threat for migratory individuals and Gulf of Mexico residents","docAbstract":"Power line corridors have been repeatedly assessed as habitat for wild bees; however, few studies have examined them as bee habitat relative to nearby crop fields and surrounding landscape context.
We surveyed bee communities in power line corridors near to and isolated from lowbush blueberry fields in two landscape contexts in Maine, U.S.A. We examined the influences of blooming plant abundance and diversity and bee life-history traits including sociality, nesting preference, and body size.
We surveyed wild bees and blooming plants in power line corridors from 2013 to 2015. We calculated landscape composition surrounding sites at multiple scales and gathered bee trait information from the literature. We assessed differences in bee communities owing to landscape context with generalized linear models.
We collected 125 wild bee species and observed a rare plant-pollinator relationship within power line corridors. We found greater bee abundance and species richness throughout a complex, resource-rich landscape, while mass-flowering lowbush blueberry fields enhanced bee species richness only in a simple, resource-poor landscape. Landscape composition and blooming plant diversity varied with landscape context, though only landscape composition influenced bee communities. Solitary and ground-nesting species were more sensitive to landscape context than social or cavity-nesting species.
Power line corridors provide crucial refugia for crop pollinating wild bees in agricultural landscapes with resource-poor natural habitat, while bees may selectively forage in power line corridors within agricultural landscapes containing resource-rich natural habitat. We found high-quality forage within corridors; quantifying nesting resources could clarify corridor use by wild bees.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01495-9","usgsCitation":"Du Clos, B., Drummond, F.A., and Loftin, C., 2022, Effects of an early mass-flowering crop on wild bee communities and traits in power line corridors vary with blooming plants and landscape context: Landscape Ecology, v. 37, p. 2619-2634, https://doi.org/10.1007/s10980-022-01495-9.","productDescription":"16 p.","startPage":"2619","endPage":"2634","ipdsId":"IP-124416","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":428535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"37","noUsgsAuthors":false,"publicationDate":"2022-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Du Clos, Brianne","contributorId":336548,"corporation":false,"usgs":false,"family":"Du Clos","given":"Brianne","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":900270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drummond, Francis A.","contributorId":336549,"corporation":false,"usgs":false,"family":"Drummond","given":"Francis","email":"","middleInitial":"A.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":900271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":900269,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70235722,"text":"70235722 - 2022 - Tracking geomorphic changes after suburban development with a high density of green stormwater infrastructure practices in Montgomery County, Maryland","interactions":[],"lastModifiedDate":"2022-08-16T11:50:42.860448","indexId":"70235722","displayToPublicDate":"2022-08-11T06:46:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Tracking geomorphic changes after suburban development with a high density of green stormwater infrastructure practices in Montgomery County, Maryland","docAbstract":"Stream morphology is affected by changes on the surrounding landscape. Understanding the effects of urbanization on stream morphology is a critical factor for land managers to maintain and improve vulnerable stream corridors in urbanizing landscapes. Stormwater practices are used in urban landscapes to manage runoff volumes and peak flows, potentially mitigating alterations to the flow regime that drive changes in channel morphology. However, there remains a paucity of long-term studies assessing watershed-scale relationships between urbanization and effects on stream morphology where green stormwater infrastructure exists in high densities. This paper evaluates the geomorphic changes across four headwater catchments in the Chesapeake Bay Watershed over the course of >10 yr and relates these changes to urban development. Annual cross-sectional surveys conducted from 2002 to 2019 in one forested catchment, one agricultural catchment, and two treatment catchments were used to understand the relationship between urbanization and changes in stream morphology. Six cross-sectional geomorphic metrics were calculated and compared with development timelines and high flow events. A channel evolution model was then used to understand the status of morphologic stability at sites within the study. Results suggest downstream environments in developing areas are more impacted during early phases of suburban construction. Channel change during construction could be a result of sediment and erosion control efforts' limitations on preventing and controlling overland sediment mobilization or of increased discharge causing widening and thus bank-derived sediment to move to the streambed. Results demonstrate that geomorphic metrics are highly variable within a small area and are not always accurate representations of broader landscape changes but rather of the more localized environment at a specific stream segment. Despite a high density of stormwater management facilities in urban catchments, substantial alterations to cross sections were found at multiple locations in each catchment including the controls.
This study utilizes instruments from the Curiosity rover payload to develop an integrated paleoenvironmental and compositional reconstruction for the 65-m thick interval of stratigraphy comprising the Hartmann's Valley and Karasburg members of the Murray formation, Gale crater, Mars. The stratigraphy consists of cross-stratified sandstone (Facies 1), planar-laminated sandstone (Facies 2), and planar-laminated mudstone (Facies 3). Facies 1 is composed of sandstone showing truncated sets of concave-curvilinear laminae stacked into cosets. Sets are estimated to be meter-to sub-meter-scale, consistent with low-height dunes. Thin stratigraphic intervals of Facies 1 and stacking patterns with Facies 2 and 3 support a wet aeolian dune interpretation. Meter-thick packages of planar-laminated sandstone (Facies 2) are interpreted to represent interfingering dune-interdune strata. Facies 3 consists of meter-thick packages of planar-laminated mudstone interpreted to represent lacustrine deposition with persistent standing water. Integration of geochemistry with each facies reveals some compositional control based on the depositional process. Models for source rock composition from Alpha Particle X-Ray Spectrometer measurements show that facies derived from a basaltic source. Alteration indices and geochemical trends provide evidence that moderate chemical weathering occurred before compositional changes due to diagenesis. Differences in wt% FeO(T) and TiO2 between facies are minimal, though trends point to sediment sorting in transport. Comparisons to terrestrial basaltic sedimentary systems indicate that the Hartmann's Valley and Karasburg facies reflect deposition in an environment where diverse subaqueous and subaerial facies persisted adjacent to a long-lived body of water.
Navigating the space between policy and on-the-ground natural resource management presents unique challenges. We interviewed 22 U.S. Bureau of Land Management Field Office Managers to understand their perceptions toward, and applications of, collaboration with public and private stakeholders. Interviews were transcribed and open-coded using qualitative data analysis software. Then, each interview was represented visually using the MaxQDA MaxMaps feature. We deductively coded each visual model and created a typology based on a mix of salient traits exhibited by each group. Differences emerged in each group’s approach to teaching and learning; communication style; attitude toward collaboration; attention to relational and substantive outcomes; and the ability to create space within the agency mission to achieve mutually beneficial goals. Findings can help agencies navigate the challenges associated with aligning agency directives with on-the-ground realities in different contexts when collaborators exhibit different traits.
The rapid expansion of CubeSat constellations could revolutionize the way inland and nearshore coastal waters are monitored from space. This potential stems from the ability of CubeSats to provide daily imagery with global coverage at meter-scale spatial resolution. In this study, we explore the unique opportunity to improve the retrieval of bathymetry offered by CubeSats, specifically those of the PlanetScope constellation. The orbital design of the PlanetScope constellation enables the acquisition of image sequences with short time lags (from seconds to hours). This characteristic allows multiple images to be captured during a short period of steady bathymetric conditions, especially in dynamic environments like rivers. We hypothesize that taking the ensemble mean of a CubeSat image sequence can enhance bathymetry retrieval compared to standard single-image analysis. Along with the existing optimal band ratio analysis (OBRA) algorithm, we also use a new neural network-based depth retrieval (NNDR) technique to infer bathymetry from both individual and time-averaged images. The two methodologies are evaluated using field data from five different river reaches with depths up to 15 m and both top-of-atmosphere (TOA) radiance and bottom-of-atmosphere (BOA) surface reflectance PlanetScope data products. Despite low spectral resolution and concerns about the radiometric quality of CubeSat imagery, accuracy assessment based on in-situ comparisons indicates the potential (0.52 < R2 < 0.7 for the NNDR method) of PlanetScope imagery to retrieve depths up to ∼ 10 m in clear water conditions. The proposed image averaging consistently improves bathymetry retrieval over single image analysis. The NNDR technique was found to outperform OBRA, illustrating the importance of leveraging all spectral bands through machine learning approaches. TOA data provided more robust bathymetry results than BOA data for the OBRA technique, but the NNDR technique was minimally impacted by the type of data product.
Mineral commodity supply disruptions have the potential to ripple through and impact the economy in many ways. Industrial vulnerability is a crucial component of mineral commodity criticality tools as it provides guidance on the economic importance of these commodities to regional criticality indices. Using an economic model that links mineral commodity end-use data to input-output tables and a linear optimization routine, reductions in economic output of individual industries and of the overall economy may be calculated. Such a model can also help to identify industries, be they direct or indirect consumers of the mineral commodities in question, that are most vulnerable to specific mineral commodity supply disruptions at different disruption magnitudes. In this assessment, 56 commodities’ end-use data for the year 2012 were paired with the United States’ detail-level Benchmark Input-Output accounts to build an industrial vulnerability model. The model does not evaluate the likelihood of specific supply disruptions but can be used to assess potential industry impacts for a range of scenarios. The model findings indicate that when the supplies of mineral commodities such as mica, lithium, and fluorspar were disrupted, large overall economic decline was paired with a large decline in many industries. On the other hand, gold, lead, and rhenium disruptions resulted in low declines and few disrupted industries.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.resourpol.2022.102889","usgsCitation":"Manley, R., Alonso, E., and Nassar, N.T., 2022, A model to assess industry vulnerability to disruptions in mineral commodity supplies: Resources Policy, v. 78, 102889, 10 p., https://doi.org/10.1016/j.resourpol.2022.102889.","productDescription":"102889, 10 p.","ipdsId":"IP-135103","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":446828,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.resourpol.2022.102889","text":"Publisher Index Page"},{"id":408479,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"78","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Manley, Ross 0000-0002-3341-4766","orcid":"https://orcid.org/0000-0002-3341-4766","contributorId":223012,"corporation":false,"usgs":true,"family":"Manley","given":"Ross","email":"","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":854815,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alonso, Elisa 0000-0002-0090-8284","orcid":"https://orcid.org/0000-0002-0090-8284","contributorId":223015,"corporation":false,"usgs":true,"family":"Alonso","given":"Elisa","email":"","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":854816,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nassar, Nedal T. 0000-0001-8758-9732 nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":197864,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal","email":"nnassar@usgs.gov","middleInitial":"T.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":854817,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70234378,"text":"70234378 - 2022 - Deciphering natural and anthropogenic nitrate and recharge sources in arid region groundwater","interactions":[],"lastModifiedDate":"2022-08-10T13:47:50.641199","indexId":"70234378","displayToPublicDate":"2022-08-10T08:39:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Deciphering natural and anthropogenic nitrate and recharge sources in arid region groundwater","docAbstract":"Recently, the subsoils of ephemeral stream (arroyos) floodplains in the northern Chihuahuan Desert were discovered to contain large naturally occurring NO3− reservoirs (floodplain: ~38,000 kg NO3-N/ha; background: ~60 kg NO3-N/ha). These reservoirs may be mobilized through land use change or natural stream channel migration which makes differentiating between anthropogenic and natural groundwater NO3− sources challenging. In this study, the fate and sources of NO3− were investigated in an area with multiple NO3− sources such as accidental sewer line releases and sewage lagoons as well as natural reservoirs of subsoil NO3−. To differentiate sources, this study used a large suite of geochemical tools including δ15N[NO3], δ18O[NO3], δ15N[N2], δ13C[DIC], 14C, tritium (3H), dissolved gas concentrations, major ion chemistry, and contaminants of emerging concern (CEC) including artificial sweeteners. NO3− at sites with the highest concentrations (25 to 229 mg/L NO3-N) were determined to be largely sourced from naturally occurring subsoil NO3− based on δ15N[NO3] (<8 ‰) and mass ratios of Cl−/Br− (〈100) and NO3−/Cl− (>1.5). Anthropogenic NO3− was deciphered using mass ratios of Cl−/Br− (>120) and NO3−/Cl− (<1), δ15N[NO3] (>8 ‰), and CEC detections. Nitrogen isotope analyses indicated that denitrification is fairly limited in the field area. CEC were detected at 67 % of sites including 3H dead sites (<1 pCi/L) with low percent modern carbon-14 (PMC; <30 %). Local supply wells are 3H dead with low PMC; as 3H does not re-equilibrate and 14C is very slow to re-equilibrate during recirculation through infrastructure, sites with low PMC, 3H < 1 pCi/L, and CEC detections were interpreted as locations with substantial anthropogenic groundwater recharge. Neotame was used to identify locations of very recent (<15 years before present) or ongoing wastewater influxes to the aquifer. This work shows the important influence of naturally occurring subsoil NO3− reservoirs on groundwater in arid regions and the major contribution of artificial recharge.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.157345","usgsCitation":"Linhoff, B.S., 2022, Deciphering natural and anthropogenic nitrate and recharge sources in arid region groundwater: Science of the Total Environment, v. 848, 157345, 16 p., https://doi.org/10.1016/j.scitotenv.2022.157345.","productDescription":"157345, 16 p.","ipdsId":"IP-137249","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":446835,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2022.157345","text":"Publisher Index Page"},{"id":405070,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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Mexico\",\"nation\":\"USA \"}}]}","volume":"848","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Linhoff, Benjamin S. 0000-0002-9478-7558","orcid":"https://orcid.org/0000-0002-9478-7558","contributorId":215020,"corporation":false,"usgs":true,"family":"Linhoff","given":"Benjamin","email":"","middleInitial":"S.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":848738,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70237185,"text":"70237185 - 2022 - Millennia-old coral holobiont DNA provides insight into future adaptive trajectories","interactions":[],"lastModifiedDate":"2022-10-04T12:25:49.925671","indexId":"70237185","displayToPublicDate":"2022-08-09T07:21:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2774,"text":"Molecular Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Millennia-old coral holobiont DNA provides insight into future adaptive trajectories","docAbstract":"Ancient DNA (aDNA) has been applied to evolutionary questions across a wide variety of taxa. Here, for the first time, we leverage aDNA from millennia-old fossil coral fragments to gain new insights into a rapidly declining western Atlantic reef ecosystem. We sampled four Acropora palmata fragments (dated 4215 BCE - 1099 CE) obtained from two Florida Keys reef cores. From these samples, we established that it is possible both to sequence ancient DNA from reef cores and place the data in the context of modern-day genetic variation. We recovered varying amounts of nuclear DNA exhibiting the characteristic signatures of aDNA from the A. palmata fragments. To describe the holobiont sensu lato, which plays a crucial role in reef health, we utilized metagenome-assembled genomes as a reference to identify a large additional proportion of ancient microbial DNA from the samples. The samples shared many common microbes with modern-day coral holobionts from the same region, suggesting remarkable holobiont stability over time. Despite efforts, we were unable to recover ancient Symbiodiniaceae reads from the samples. Comparing the ancient A. palmata data to whole-genome sequencing data from living acroporids, we found that while slightly distinct, ancient samples were most closely related to individuals of their own species. Together, these results provide a proof-of-principle showing that it is possible to carry out direct analysis of coral holobiont change over time, which lays a foundation for studying the impacts of environmental stress and evolutionary constraints.","language":"English","publisher":"Wiley","doi":"10.1111/mec.16642","usgsCitation":"Scott, C.B., Cardenas, A., Mah, M., Narasimhan, V., Rohland, N., Toth, L., Voostra, C., Reich, D., and Matz, M.V., 2022, Millennia-old coral holobiont DNA provides insight into future adaptive trajectories: Molecular Ecology, v. 31, no. 19, p. 4979-4990, https://doi.org/10.1111/mec.16642.","productDescription":"12 p.","startPage":"4979","endPage":"4990","ipdsId":"IP-132992","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":446852,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://nbn-resolving.de/urn:nbn:de:bsz:352-2-jkfsqqrf91776","text":"External Repository"},{"id":407855,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"19","noUsgsAuthors":false,"publicationDate":"2022-08-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Scott, Carly B.","contributorId":297168,"corporation":false,"usgs":false,"family":"Scott","given":"Carly","email":"","middleInitial":"B.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":853590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cardenas, Anny","contributorId":297169,"corporation":false,"usgs":false,"family":"Cardenas","given":"Anny","email":"","affiliations":[{"id":55536,"text":"University of Konstanz","active":true,"usgs":false}],"preferred":false,"id":853591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mah, Matthew","contributorId":297170,"corporation":false,"usgs":false,"family":"Mah","given":"Matthew","email":"","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":853592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Narasimhan, Vagheesh","contributorId":297171,"corporation":false,"usgs":false,"family":"Narasimhan","given":"Vagheesh","email":"","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":853593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rohland, Nadin","contributorId":297173,"corporation":false,"usgs":false,"family":"Rohland","given":"Nadin","email":"","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":853594,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":853595,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Voostra, Christian","contributorId":297175,"corporation":false,"usgs":false,"family":"Voostra","given":"Christian","email":"","affiliations":[{"id":55536,"text":"University of Konstanz","active":true,"usgs":false}],"preferred":false,"id":853596,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reich, David","contributorId":297177,"corporation":false,"usgs":false,"family":"Reich","given":"David","email":"","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":853597,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Matz, Mikhail V","contributorId":243005,"corporation":false,"usgs":false,"family":"Matz","given":"Mikhail","email":"","middleInitial":"V","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":853598,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70238066,"text":"70238066 - 2022 - Projecting flood frequency curves under near-term climate change","interactions":[],"lastModifiedDate":"2022-11-08T12:38:08.023903","indexId":"70238066","displayToPublicDate":"2022-08-09T06:35:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Projecting flood frequency curves under near-term climate change","docAbstract":"Flood-frequency curves, critical for water infrastructure design, are typically developed based on a stationary climate assumption. However, climate changes are expected to violate this assumption. Here, we propose a new, climate-informed methodology for estimating flood-frequency curves under non-stationary future climate conditions. The methodology develops an asynchronous, semiparametric local-likelihood regression (ASLLR) model that relates moments of annual maximum flood to climate variables using the generalized linear model. We estimate the first two marginal moments (MM) – the mean and variance – of the underlying log-Pearson Type-3 distribution from the ASLLR with the monthly rainfall and temperature as predictors. The proposed methodology, ASLLR-MM, is applied to 40 U.S. Geological Survey streamgages covering 18 water resources regions across the conterminous United States. A correction based on the aridity index was applied on the estimated variance, after which the ASLLR-MM approach was evaluated with both historical (1951–2005) and projected (2006–2035, under RCP4.5 and RCP8.5) monthly precipitation and temperature from eight Global Circulation Models (GCMs) consisting of 39 ensemble members. The estimated flood-frequency quantiles resulting from the ASLLR-MM and GCM members compare well with the flood-frequency quantiles estimated using the historical period of observed climate and flood information for humid basins, whereas the uncertainty in model estimates is higher in arid basins. Considering additional atmospheric and land-surface conditions and a multi-level model structure that includes other basins in a region could further improve the model performance in arid basins.
Early detection of dreissenid mussels (Dreissena polymorpha and D. rostriformis bugensis) is crucial to mitigating the economic and environmental impacts of an infestation. Plankton tow sampling is a common method used for early detection of dreissenid mussels, but little is known about the sampling intensity required for a high probability of early detection using the method. We used implicit dynamic occupancy models to estimate plankton tow detection probabilities of dreissenid mussels from a long-term data set containing plankton tow samples collected across central and western United States. We fit models using a) the entire data set, including water bodies with unknown occupancy status in addition to heavily infested water bodies, b) a data subset that included water bodies with paired water temperature data, and c) a data subset that included water bodies with lower dreissenid densities. For the entire data set, we found that estimated detection probabilities varied by water body size and ranged from approximately 0.10 to 0.86. For the water temperature subset, we observed the same pattern between detection probability and water body size as we did for the full data but additionally found that the estimated detection probabilities were much higher when water temperatures were above 12 °C. For the lower dreissenid density subset, we found that the estimated probability of detecting dreissenid mussels with a single aggregated plankton tow sample was near zero. Given these estimates, we conclude that the number of aggregated plankton tow samples taken per water body in the data is far fewer than the number needed to ensure a high probability of detecting dreissenid mussels, especially if they are at low densities. We summarize the analyses with a discussion of plankton tow sampling protocol changes needed to improve estimates of dreissenid detection probabilities.
","language":"English","publisher":"REABIC","doi":"10.3391/mbi.2022.13.4.05","usgsCitation":"Winder, M., Sepulveda, A., and Hoegh, A., 2022, An initial assessment of plankton tow detection probabilities for dreissenid mussels in the western United States: Management of Biological Invasions, v. 13, no. 4, p. 659-678, https://doi.org/10.3391/mbi.2022.13.4.05.","productDescription":"20 p.","startPage":"659","endPage":"678","ipdsId":"IP-137748","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":446857,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2022.13.4.05","text":"Publisher Index Page"},{"id":405680,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.5703125,\n 34.016241889667015\n ],\n [\n -94.5703125,\n 37.09023980307208\n ],\n [\n -94.482421875,\n 39.639537564366684\n ],\n [\n -95.888671875,\n 40.84706035607122\n ],\n [\n -96.591796875,\n 42.94033923363181\n ],\n [\n -97.20703125,\n 49.15296965617042\n ],\n [\n -123.04687499999999,\n 49.15296965617042\n ],\n [\n -123.3984375,\n 48.16608541901253\n ],\n [\n -124.8046875,\n 48.22467264956519\n ],\n [\n -124.541015625,\n 40.245991504199026\n ],\n [\n -123.57421875,\n 38.34165619279595\n ],\n [\n -121.9921875,\n 35.60371874069731\n ],\n [\n -119.00390625,\n 33.358061612778876\n ],\n [\n -116.630859375,\n 32.69486597787505\n ],\n [\n -110.302734375,\n 31.203404950917395\n ],\n [\n -108.19335937499999,\n 31.42866311735861\n ],\n [\n -106.5234375,\n 31.80289258670676\n ],\n [\n -103.0078125,\n 32.39851580247402\n ],\n [\n -103.0078125,\n 36.38591277287651\n ],\n [\n -99.931640625,\n 36.4566360115962\n ],\n [\n -99.755859375,\n 34.30714385628804\n ],\n [\n -94.5703125,\n 34.016241889667015\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Winder, Meaghan","contributorId":295487,"corporation":false,"usgs":false,"family":"Winder","given":"Meaghan","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":849583,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":849584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoegh, Andrew","contributorId":265906,"corporation":false,"usgs":false,"family":"Hoegh","given":"Andrew","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":849585,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239346,"text":"70239346 - 2022 - Electrical imaging for hydrogeology","interactions":[],"lastModifiedDate":"2023-01-10T14:46:39.606987","indexId":"70239346","displayToPublicDate":"2022-08-08T08:29:37","publicationYear":"2022","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":15,"text":"Monograph"},"title":"Electrical imaging for hydrogeology","docAbstract":"Geophysical methods offer hydrogeologists unprecedented access to understanding subsurface parameters and processes. In this book, we outline the theory and application of electrical imaging methods, which inject current into the ground and measure the resultant potentials. These data are sensitive to rock type, grain size, porosity, pore fluid electrical conductivity, saturation, and temperature. Here, we describe the physical basis for electrical imaging, parallels between electrical flow equations and the groundwater flow equation, practical considerations for field investigations, data processing and inverse modeling of field data, and how to QA/QC data. We additionally cover two case studies, including a 2-D waterborne survey and a 4-D dataset from a biostimulation experiment.
","language":"English","publisher":"The Groundwater Project","usgsCitation":"Singha, K., Johnson, T.C., Day-Lewis, F., and Slater, L., 2022, Electrical imaging for hydrogeology, xi, 74 p.","productDescription":"xi, 74 p.","ipdsId":"IP-127811","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":411626,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":411625,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://gw-project.org/books/electrical-imaging-for-hydrogeology/"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Singha, Kamini 0000-0002-0605-3774","orcid":"https://orcid.org/0000-0002-0605-3774","contributorId":191366,"corporation":false,"usgs":false,"family":"Singha","given":"Kamini","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":861207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Timothy C.","contributorId":199842,"corporation":false,"usgs":false,"family":"Johnson","given":"Timothy","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":861209,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":861208,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Slater, Lee D.","contributorId":255454,"corporation":false,"usgs":false,"family":"Slater","given":"Lee D.","affiliations":[{"id":39626,"text":"Rutgers University Newark","active":true,"usgs":false}],"preferred":false,"id":861210,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238480,"text":"70238480 - 2022 - Reference genome of the California glossy snake, Arizona elegans occidentalis: A declining California Species of Special Concern","interactions":[],"lastModifiedDate":"2022-12-01T16:23:17.773293","indexId":"70238480","displayToPublicDate":"2022-08-08T07:24:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2333,"text":"Journal of Heredity","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Reference genome of the California glossy snake, Arizona elegans occidentalis: A declining California Species of Special Concern","title":"Reference genome of the California glossy snake, Arizona elegans occidentalis: A declining California Species of Special Concern","docAbstract":"The glossy snake (Arizona elegans) is a polytypic species broadly distributed across southwestern North America. The species occupies habitats ranging from California’s coastal chaparral to the shortgrass prairies of Texas and southeastern Nebraska, to the extensive arid scrublands of central México. Three subspecies are currently recognized in California, one of which is afforded state-level protection based on the extensive loss and modification of its preferred alluvial coastal scrub and inland desert habitat. We report the first genome assembly of A. elegans occidentalis as part of the California Conservation Genomics Project (CCGP). Consistent with the reference genome strategy of the CCGP, we used Pacific Biosciences HiFi long reads and Hi-C chromatin-proximity sequencing technologies to produce a de novo assembled genome. The assembly comprises a total of 140 scaffolds spanning 1,842,602,218 base pairs, has a contig NG50 of 61 Mb, a scaffold NG50 of 136 Mb, and a BUSCO complete score of 95.9%, and is one of the most complete snake genome assemblies. The A. e. occidentalis genome will be a key tool for understanding the genomic diversity and the basis of adaptations within this species and close relatives within the hyperdiverse snake family Colubridae.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/jhered/esac040","usgsCitation":"Wood, D.A., Richmond, J.Q., Escalona, M., Marimuthu, M.P., Nguyen, O., Sacco, S., Beraut, E., Westphal, M.F., Fisher, R., Vandergast, A.G., Toffelmier, E., Wang, I., and Shaffer, H., 2022, Reference genome of the California glossy snake, Arizona elegans occidentalis: A declining California Species of Special Concern: Journal of Heredity, v. 113, no. 6, p. 632-640, https://doi.org/10.1093/jhered/esac040.","productDescription":"9 p.","startPage":"632","endPage":"640","ipdsId":"IP-143455","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":446864,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9923794","text":"External Repository"},{"id":409680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.11935248282103,\n 32.53882373045977\n ],\n [\n -114.50243196034603,\n 32.72786950214554\n ],\n [\n -114.64233250350489,\n 33.1520790618809\n ],\n [\n -114.58470276618635,\n 33.51373515511381\n ],\n [\n -114.41452412993016,\n 34.10031267745222\n ],\n [\n -114.11907829718999,\n 34.32357161601179\n ],\n [\n -114.67741209652053,\n 35.09778131876418\n ],\n [\n -117.76088232794436,\n 37.33676457017539\n ],\n [\n -119.42981485345024,\n 35.62249205151011\n ],\n [\n -121.63638617466606,\n 38.725574279970715\n ],\n [\n -122.54328366670836,\n 38.42830074064648\n ],\n [\n -119.57711374079892,\n 34.87314120906966\n ],\n [\n -118.19555417338168,\n 34.27246184404002\n ],\n [\n -117.90704702548453,\n 33.82510987924552\n ],\n [\n -117.26765483662936,\n 32.80899171054767\n ],\n [\n -116.97534971889436,\n 32.510621812966775\n ],\n [\n -117.11935248282103,\n 32.53882373045977\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"113","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857588,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richmond, Jonathan Q. 0000-0001-9398-4894 jrichmond@usgs.gov","orcid":"https://orcid.org/0000-0001-9398-4894","contributorId":5400,"corporation":false,"usgs":true,"family":"Richmond","given":"Jonathan","email":"jrichmond@usgs.gov","middleInitial":"Q.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Escalona, Merly","contributorId":299346,"corporation":false,"usgs":false,"family":"Escalona","given":"Merly","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":857590,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marimuthu, Mohan P. 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